Systems and methods for analytical comparison and monitoring of aneurysms

ABSTRACT

Systems and methods for identifying growth of a cerebral aneurysm. The method includes forming a first virtual skeleton model from a first image segmentation and forming a second virtual skeleton model from a second image segmentation. The virtual skeleton models including a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of a plurality of blood vessel surface points. The method includes identifying one or more second terminal points within the second virtual skeleton model and overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.

FIELD OF THE DISCLOSURE

The present invention is generally related to systems and methods forthe detection and analysis of aneurysms occurring in blood vessels ofthe human body including systems and methods for analytical comparisonand monitoring of aneurysms. The present invention more particularlyrelates to systems and methods for detecting aneurysms in the body andthen determining the risk of the detected aneurysm to the patient.

BACKGROUND

Aneurysm may occur in blood vessel walls of, for example, the aorta, theabdomen, the brain, capillaries, the heart, the kidneys, and the legs.If undiagnosed or left untreated, the blood-filled aneurysm may rupturethe wall of the blood vessel into the surrounding tissue possiblyleading to death.

An abdominal aortic aneurysm (“AAA”) involves a regional dilation of theaorta. Current technology for detection includes ultrasonography,computed tomography, and magnetic resonance imaging. These technologiesare employed to size segments of the aorta and compare those segment toa healthy individual. AAAs are classified by both size and shape. An AAAis usually defined as an outer aortic diameter over 3 cm or more than50% of normal diameter. The suprarenal aorta normally measures about 0.5cm larger than the infrarenal aorta. If the outer diameter of the AAAexceeds 5.5 cm, the aneurysm is considered to be large. The presence ofabdominal pain, shock, and a pulsatile abdominal mass may indicate theAAA is ruptured. As high as 90 percent of patients die before treatmentcan be administered for the rupture.

A cerebral aneurysm is a cerebral vascular disorder in which weakness ofthe wall of a cerebral artery or vein causes a localized dilation orballooning of the blood vessel wall. Cerebral aneurysms are classifiedby both size and shape. Smaller aneurysms produce few, if any, symptoms.Larger aneurysms may cause severe headaches, nausea, vision impairment,vomiting and/or loss of consciousness. Larger aneurysms have greatertendency to rupture, but, the majority of ruptured aneurysms are small.About 44 percent of patients die within 1 month after rupture. Inaddition, ruptured cerebral aneurysms account for about 10 percent ofall strokes.

Surgical treatment may be employed for those that survive the rupture ofan AAA or cerebral aneurysm. Often the surgical procedure must beemployed as soon as possible after rupture for optimal results.

Endovascular treatment involves the insertion of a medical device, suchas a coil, inside the aneurysm balloon or inside the affected bloodvessel to prevent rebleeding. This procedure is performedintraluminally. Oftentimes, a stent, which is basically an expandablehollow bridge, is used to assist in deploying the coil into the aneurysmsac. The treatment works by promoting blood clotting around the coils,eventually sealing the aneurysm and reducing pressure on its outer wall.

In addition to coils, other endovascular treatments include use of flowdiverter stents. Flow diverter stent devices block the opening at thebase of the aneurysm (where it meets the vessel wall), preventing bloodfrom flowing into the aneurysm sac. Unfortunately, endovasculartreatments carry greater risks when employed post rupture.

If diagnosed prior to rupture, the endovascular treatment may beperformed on the patient and mitigate against rupture occurrence. Thechallenge lies in correctly diagnosing the aneurysm prior to rupture.Technology in medical imaging for early diagnosis of aneurysms oftenrelies on simple comparisons between measured segments of the patientwith those of a healthy individual. In certain cases, detection of acerebral aneurysm begins by reviewing images from 3Drotational-angiography (3DRA), computed tomography angiography (CTA),and/or magnetic resonance angiography (MRA). The detection process theninvolves a laborious process by a medical provider to visually analyzethe images from different geometric points of view with multipleadjustments of contrast/brightness filters. As part of the detectionprocess, the medical provider often manually determines a volume of theaneurysm for further assessment. As such, there is a need to improvediagnostic methods for identifying aneurysms.

SUMMARY

The devices of the present invention have several features, no singleone of which is solely responsible for its desirable attributes. Withoutlimiting the scope of this invention as expressed by the claims whichfollow, its more prominent features will now be discussed briefly. Afterconsidering this discussion, and particularly after reading the sectionentitled “Detailed Description,” one will understand how the features ofthis invention provide several advantages over current designs.

An aspect of the present disclosure provides a method for identifyinggrowth of a cerebral aneurysm. The method includes providing a firstimage segmentation of a surface of a blood vessel network. The firstimage segmentation comprises a first plurality of blood vessel surfacepoints. The method includes forming a first virtual skeleton model fromthe first image segmentation. The first virtual skeleton model comprisesa plurality of edges. Each edge of the plurality of edges has aplurality of skeleton points. Each skeleton point of the plurality ofskeleton points is associated with a subset of the plurality of bloodvessel surface points. The method includes identifying one or more firstnodes within the first virtual skeleton model. Each of the one or morefirst nodes intersects more than two edges of the plurality of edges.The method includes providing a second image segmentation of a surfaceof the blood vessel network. The second image segmentation comprises asecond plurality of blood vessel surface points. The method includesforming a second virtual skeleton model from the second imagesegmentation. The second virtual skeleton model comprises a plurality ofedges. Each edge of the plurality of edges has a plurality of skeletonpoints. Each skeleton point of the plurality of skeleton points isassociated with a subset of the plurality of blood vessel surfacepoints. The method includes identifying one or more second nodes withinthe second virtual skeleton model. Each of the one or more second nodesintersects more than two edges of the plurality of edges. The methodincludes overlapping the first virtual skeleton model and the secondvirtual skeleton model by orienting the one or more first nodes with theone or more second nodes.

Another aspect is comparing geometry of the cerebral aneurysm when thefirst virtual skeleton model is overlapped with the second virtualskeleton model.

Further aspects are identifying one or more first terminal points withinthe first virtual skeleton model, each of the one or more first terminalpoints intersecting only one edge of the plurality of edges, identifyingone or more second terminal points within the second virtual skeletonmodel, each of the one or more second terminal points intersecting onlyone edge of the plurality of edges, and overlapping the first virtualskeleton model and the second virtual skeleton model by orienting theone or more first terminal points with the one or more second terminalpoints.

Another aspect is wherein the one or more first nodes is a bifurcationpoint.

Another aspect is wherein the one or more first nodes is part of acycle.

Another aspect of the present disclosure provides a system foridentifying growth of a cerebral aneurysm. The system comprises one ormore processors configured to provide a first image segmentation of asurface of a blood vessel network. The first image segmentationcomprises a first plurality of blood vessel surface points. The one ormore processors are further configured to form a first virtual skeletonmodel from the first image segmentation. The first virtual skeletonmodel comprises a plurality of edges. Each edge of the plurality ofedges has a plurality of skeleton points. Each skeleton point of theplurality of skeleton points is associated with a subset of theplurality of blood vessel surface points. The one or more processors arefurther configured to identify one or more first nodes within the firstvirtual skeleton model. Each of the one or more first nodes intersectsmore than two edges of the plurality of edges. The one or moreprocessors are further configured to provide a second image segmentationof a surface of the blood vessel network. The second image segmentationcomprises a second plurality of blood vessel surface points. The one ormore processors are further configured to form a second virtual skeletonmodel from the second image segmentation. The second virtual skeletonmodel comprises a plurality of edges. Each edge of the plurality ofedges has a plurality of skeleton points. Each skeleton point of theplurality of skeleton points is associated with a subset of theplurality of blood vessel surface points. The one or more processors arefurther configured to identify one or more second nodes within thesecond virtual skeleton model. Each of the one or more second nodesintersects more than two edges of the plurality of edges. The one ormore processors are further configured to overlap the first virtualskeleton model and the second virtual skeleton model by orienting theone or more first nodes with the one or more second nodes.

Another aspect is wherein the one or more processors are furtherconfigured to compare geometry of the cerebral aneurysm when the firstvirtual skeleton model is overlapped with the second virtual skeletonmodel.

Further aspects are wherein the one or more processors are furtherconfigured to identify one or more first terminal points within thefirst virtual skeleton model, each of the one or more first terminalpoints intersecting only one edge of the plurality of edges, identifyone or more second terminal points within the second virtual skeletonmodel, each of the one or more second terminal points intersecting onlyone edge of the plurality of edges, and overlap the first virtualskeleton model and the second virtual skeleton model by orienting theone or more first terminal points with the one or more second terminalpoints.

Another aspect is wherein the one or more first nodes is a bifurcationpoint.

Another aspect is wherein the one or more first nodes is part of acycle.

Another aspect is wherein the one or more second nodes is a bifurcationpoint.

Another aspect is wherein the one or more second nodes is part of acycle.

Another aspect of the present disclosure provides a method foridentifying growth of a cerebral aneurysm. The method comprisesproviding a first image segmentation of a surface of a blood vesselnetwork. The first image segmentation comprises a first plurality ofblood vessel surface points. The method includes forming a first virtualskeleton model from the first image segmentation. The first virtualskeleton model comprises a plurality of edges. Each edge of theplurality of edges has a plurality of skeleton points. Each skeletonpoint of the plurality of skeleton points is associated with a subset ofthe plurality of blood vessel surface points. The method includesidentifying one or more first terminal points within the first virtualskeleton model. Each of the one or more first terminal points intersectsonly one edge of the plurality of edges. The method includes providing asecond image segmentation of a surface of the blood vessel network. Thesecond image segmentation comprises a second plurality of blood vesselsurface points. The method includes forming a second virtual skeletonmodel from the second image segmentation. The second virtual skeletonmodel comprises a plurality of edges. Each edge of the plurality ofedges has a plurality of skeleton points. Each skeleton point of theplurality of skeleton points is associated with a subset of theplurality of blood vessel surface points. The method includesidentifying one or more second terminal points within the second virtualskeleton model. Each of the one or more second terminal pointsintersects only one edge of the plurality of edges. The method includesoverlapping the first virtual skeleton model and the second virtualskeleton model by orienting the one or more first terminal points withthe one or more second terminal points.

Another aspect is comparing geometry of the cerebral aneurysm when thefirst virtual skeleton model is overlapped with the second virtualskeleton model.

Further aspects are identifying one or more first nodes within the firstvirtual skeleton model, each of the one or more first nodes intersectingmore than one edge of the plurality of edges, identifying one or moresecond nodes within the second virtual skeleton model, each of the oneor more second nodes intersecting more than one edge of the plurality ofedges, and overlapping the first virtual skeleton model and the secondvirtual skeleton model by orienting the one or more first nodes with theone or more second nodes.

Another aspect is wherein the one or more first nodes is a bifurcationpoint.

Another aspect is wherein the one or more first nodes is part of acycle.

Another aspect is wherein the one or more second nodes is a bifurcationpoint.

Another aspect is wherein the one or more second nodes is part of acycle.

Another aspect is wherein the one or more first nodes is a vertex.

An aspect of the present disclosure provides a method for detecting ananeurysm. The method comprises providing an image segmentation of asurface of a blood vessel network. The image segmentation comprises aplurality of blood vessel surface points. The method includes forming avirtual skeleton model from the image segmentation. The virtual skeletonmodel comprises a plurality of edges with each edge of the plurality ofedges having a plurality of skeleton points. Each skeleton point of theplurality of skeleton points is associated with a subset of theplurality of blood vessel surface points. The method includesidentifying inlets and outlets of the virtual skeleton model, virtuallyfitting elliptically shaped tubules for each edge of the skeletonizedgraph. determine statistics of the fitted elliptically shaped tubulesfor each edge, identifying a plurality of potential aneurysms based onthe determined statistics, filtering the plurality of potentialaneurysms based on the determined statistics, and identifying theaneurysm based at least in part on the filtering.

Another aspect is wherein forming the virtual skeleton model comprisessimultaneously determining a medial axis transform of the imagesegmentation and a mean curvature flow of the image segmentation.

Another aspect is wherein forming the virtual skeleton model comprisesdetermining a medial axis transform of the image segmentation.

Another aspect is wherein forming the virtual skeleton model comprisesdetermining a mean curvature flow of the image segmentation.

Another aspect is wherein identifying the inlets of the virtual skeletonmodel is based at least in part on determining a distance from eachinlet to a flat region formed by one or more of the plurality of bloodvessel surface points.

Another aspect is wherein identifying the inlets of the virtual skeletonmodel is based at least in part on an average distance from each edge tothe subset of the plurality of blood vessel surface points associatedwith the edge.

Another aspect is wherein identifying the outlets of the virtualskeleton model is based at least in part on a skeleton aspect ratio foreach edge.

Another aspect is wherein virtually fitting elliptically shaped tubulesfor each edge of the virtual skeleton model is performed at eachskeleton point of the plurality of skeleton points associated with theedge.

Another aspect is wherein determining statistics of the fittedelliptically shaped tubules for each edge comprises one or more of amean, minimum, maximum, and standard deviations.

Another aspect is wherein determining statistics of the fittedelliptically shaped tubules comprises area statistics for each edge.

Another aspect is wherein filtering the plurality of potential aneurysmsbased on the determined statistics comprises determining deviations ofareas between adjacent ellipses of the elliptically shaped tubules.

Another aspect is wherein determining statistics of the fittedelliptically shaped tubules comprises eccentricity statistics for eachedge.

Another aspect is wherein filtering the plurality of potential aneurysmsbased on the determined statistics comprises determining deviations ofeccentricities between adjacent ellipses of the elliptically shapedtubules.

Another aspect is wherein determining statistics of the fittedelliptically shaped tubules comprises volume statistics for each edge,and further comprising filtering the plurality of potential aneurysms toidentify false positive (FP) aneurysm cases based on the volumestatistics.

Another aspect of the present disclosure provides a system for aneurysmdetection and analysis. The system comprises one or more processorsconfigured to provide an image segmentation of a surface of a bloodvessel network. The image segmentation comprises a plurality of bloodvessel surface points. The one or more processors are further configuredto form a virtual skeleton model from the image segmentation. Thevirtual skeleton model comprises a plurality of edges. Each edge of theplurality of edge lines has a plurality of skeleton points. Eachskeleton point of the plurality of skeleton points is associated with asubset of the plurality of blood vessel surface points. The one or moreprocessors are further configured to virtually fit elliptically shapedtubules for each edge of the virtual skeleton model and identify theaneurysm based at least in part on the virtually fit elliptically shapedtubules.

Another aspect is wherein the one or more processors is furtherconfigured to determine statistics of the fitted elliptically shapedtubules for each edge.

Another aspect is wherein the one or more processors is furtherconfigured to identify a plurality of potential aneurysms based on thedetermined statistics.

Another aspect is wherein the one or more processors is furtherconfigured to filter the plurality of potential aneurysms based on thedetermined statistics.

Another aspect is wherein the one or more processors is furtherconfigured to identify the aneurysm based at least in part on thefiltering.

Another aspect is wherein the one or more processors is furtherconfigured to identify inlets and outlets of the skeletonized graph.

Another aspect of the present disclosure provides a method for detectingan aneurysm. The method comprises forming a virtual skeleton modelcomprising a plurality of edges. Each edge of the plurality of edges hasa plurality of skeleton points. Each skeleton point of the plurality ofskeleton points is associated with a subset of the plurality of bloodvessel surface points. The method includes virtually fittingelliptically shaped tubules for each edge of the virtual skeleton modeland identifying a potential aneurysm based on the fitted ellipticallyshaped tubules.

An aspect of the present disclosure provides a method for classifyingand measuring a cerebral aneurysm of a parent vessel. The methodincludes identifying a potential aneurysm and determining whether thepotential aneurysm is a saccular aneurysm type or a fusiform aneurysmtype. If the potential aneurysm is the saccular aneurysm type thendetermine a neck-plane, a neck width of the neck-plane, a dome height,and a dome width. If the potential aneurysm is the fusiform aneurysmtype then determining a distal plane, a proximal plane, a fusiformlength, and a fusiform width.

Another aspect includes wherein the neck-plane comprises a best fitplane that encompasses a boundary between the saccular aneurysm and theparent vessel, and wherein the best fit plane comprises a set of pointsselected at least in part on whether the set of points have a negativeGaussian curvature.

Another aspect includes wherein the dome height is a maximum distancebetween a center of the neck-plane and a point on the saccular aneurysm.

Another aspect includes wherein the dome width is a maximum aneurysmwidth measured perpendicular to a line defining the dome height.

Another aspect includes wherein the distal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Another aspect includes wherein the proximal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Another aspect includes wherein the fusiform length is a length measuredalong a path between the distal plane and the proximal plane.

Another aspect includes wherein the fusiform width is a length measuredperpendicular to the path between the distal plane and the proximalplane.

Another aspect of the present disclosure provides a system forclassifying and measuring a cerebral aneurysm of a parent vessel. Thesystem includes one or more processors configured to identify apotential aneurysm and determine whether the potential aneurysm is asaccular aneurysm type or a fusiform aneurysm type. If the potentialaneurysm is the saccular aneurysm type then determine a neck-plane, aneck width of the neck-plane, a dome height, and a dome width. If thepotential aneurysm is the fusiform aneurysm then determine a distalplane, a proximal plane, a fusiform length, and a fusiform width.

Another aspect includes wherein the neck-plane comprises a best fitplane that encompasses a boundary between the saccular aneurysm and theparent vessel, and wherein the best fit plane comprises a set of pointsselected at least in part on whether the set of points have a negativeGaussian curvature.

Another aspect includes wherein the dome height is a maximum distancebetween a center of the neck-plane and a point on the saccular aneurysm.

Another aspect includes wherein the dome width is a maximum aneurysmwidth measured perpendicular to a line defining the dome height.

Another aspect includes wherein the distal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Another aspect includes wherein the proximal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Another aspect includes wherein the fusiform length is a length measuredalong a path between the distal plane and the proximal plane.

Another aspect includes wherein the fusiform width is a length measuredperpendicular to the path between the distal plane and the proximalplane.

Another aspect of the present disclosure provides a method forclassifying and measuring a cerebral aneurysm of a parent vessel. Themethod includes identifying a potential aneurysm and determining whetherthe potential aneurysm is a saccular aneurysm type or a fusiformaneurysm type. If the potential aneurysm is the saccular aneurysm typethen determine a neck-plane and a neck width of the neck-plane. If thepotential aneurysm is the fusiform aneurysm type then determine a distalplane and a proximal plane.

Another aspect includes wherein the neck-plane comprises a best fitplane that encompasses a boundary between the saccular aneurysm and theparent vessel, and wherein the best fit plane comprises a set of pointsselected at least in part on whether the set of points have a negativeGaussian curvature.

Another aspect includes wherein the distal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Another aspect includes wherein the proximal plane comprises a best fitplane that encompasses a boundary between the fusiform aneurysm and theparent vessel.

Further aspects features and advantages of the present invention willbecome apparent from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will now be described in connection with embodiments of thepresent invention, in reference to the accompanying drawings. Theillustrated embodiments, however, are merely examples and are notintended to limit the invention. Some embodiments will be described inconjunction with the appended drawings, where like designations denotelike elements.

Embodiments of the invention will now be described, by way of exampleonly, with reference to the accompanying drawings in which correspondingreference symbols indicate corresponding parts.

FIG. 1 depicts a computer system for aneurysm detection and analysis inaccordance with an exemplary embodiment of the present invention;

FIG. 2 is an exemplary representation of any of the modules of thecomputer system including the skeletonization module, the inlet/outletmodule, the construction module, and the filtering module from FIG. 1;

FIG. 3 is a representation of an exemplary graph of a blood vesselnetwork showing certain objects of the blood vessel network;

FIG. 4A illustrates a virtual skeleton model of a blood vessel network;

FIG. 4B is a view of a portion of the blood vessel network taken alonglines 4B-4B of FIG. 4A and illustrates one or more surface points of theblood vessel network and a single skeleton point associated with the oneor more surface points;

FIG. 5A illustrates a virtual inlet/outlet model of the blood vesselnetwork illustrated in FIG. 4A;

FIG. 5B illustrates the inlet/outlet module initially masking the bloodvessel network as blood vessel, cycle, or potential aneurysm.

FIG. 6A illustrates the construction module fitting one or morepredefined geometric shapes to the virtual skeleton model created by theskeletonization module to form an ellipse model.

FIG. 6B illustrates the construction module fitting an ellipse to one ofthe skeleton points of the edge.

FIG. 7A illustrates the output of the filtering module represented by afilter model.

FIG. 7B illustrates values of ellipse areas associated with skeletonpoints at locations along the edge.

FIG. 8A illustrates the output of the filtering module after areafiltering of the ellipse.

FIG. 8B illustrates the output of the filtering module after furtherfiltering the output in FIG. 8A for variations in eccentricity of theellipses.

FIG. 9A illustrates the output of the filtering module after volumefiltering of a first potential aneurysm.

FIG. 9B illustrates the output of the filtering module after volumefiltering of a second potential aneurysm.

FIG. 10A illustrates idealized saccular measurements of a potentialaneurysm.

FIG. 10B illustrates idealized fusiform measurements of a potentialaneurysm.

FIGS. 11A and 11B illustrate changes in dimensions of a saccularaneurysm over time.

FIG. 11C illustrates the same identified aneurysm from FIGS. 11A and 11Boverlapped to show growth of the identified aneurysm.

FIGS. 12A and 12B illustrate exemplary measurements and dimensions of asaccular aneurysm.

FIG. 13 illustrates exemplary measurements and dimensions of a fusiformaneurysm.

FIG. 14 illustrates a method for cerebral aneurysm detection performedby the computer system illustrated in FIG. 1.

FIG. 15 illustrates a method performed by the skeletonization model forforming a skeletonized graph or virtual skeleton model.

FIG. 16 illustrates a method performed by the inlet/outlet module toidentify inlets points and outlets points of the skeletonized graph orvirtual skeleton model.

FIG. 17 illustrates a method performed by the construction module toidentify potential aneurysm.

FIG. 18 illustrates a method performed by the filtering module to filterpotential aneurysm identified by the construction module.

FIG. 19 illustrates a method of identifying a cerebral aneurysm from animage segmentation.

FIG. 20 illustrates a method of measuring a cerebral aneurysm dependingon whether the aneurysm is a saccular or fusiform aneurysm.

FIG. 21 illustrates a method for comparing geometry of a cerebralaneurysm over a period of time.

The various features illustrated in the drawings may not be drawn toscale. Accordingly, the dimensions of the various features may bearbitrarily expanded or reduced for clarity. In addition, some of thedrawings may not depict all of the components of a given system, methodor device. Finally, like reference numerals may be used to denote likefeatures throughout the specification and figures.

DETAILED DESCRIPTION

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

Definitions

As used in this description and the accompanying claims, the followingterms shall have the meanings indicated, unless the context otherwiserequires:

Graph is a mathematical structure used to model pairwise relationsbetween objects (like nodes, points, vertices etc . . . ). Pairwiserelations between relations itself are called edges (also called linksor lines).

Bifurcation in context of graph is a node that has more than two edgesattached.

Terminal point denotes a node (or point) that has only one edge/lineattached.

Path is a collection of edges/lines (can be just one edge) connectingtwo nodes or points.

Cycle in graph is a path of edges and nodes wherein a vertex isreachable from itself.

Inlet/outlet points are terminal points of blood vessel network,represented as graph, and denote blood flow inlets/outlets.

Segmentation is a surface representation of blood vessel networkobtained from raw pixel image.

Overview

FIG. 1 depicts a computer system 100 for aneurysm detection and analysisin accordance with an exemplary embodiment of the present invention. Incertain embodiments, the computer system 100 comprises one or more of askeletonization module 102, an inlet/outlet module 104, a constructionmodule 106, and a filtering module 108. In certain embodiments, thecomputer system 100 comprises one or more of an image apparatus 110, adisplay device 112, and a user input 114.

As will be explained in more detail with respect to FIGS. 4A and 4B, theskeletonization module 102 is configured to create a virtual skeletonmodel of a blood vessel network. In certain embodiments, the virtualskeleton model is represented by a graph. In certain embodiments, thegraph is a mathematical structure used to model pairwise relationsbetween objects. Objects can include, for example, nodes, points, andvertices within the virtual skeleton model. Pairwise relations betweenrelations itself are called edges. Edges can include links or lineswithin the virtual skeleton model.

In certain embodiments, the virtual skeleton model of the blood vesselnetwork is at least created in part based on images taken of the bloodvessel network. In certain embodiments, the virtual skeleton modelrepresents the entire blood vessel network. In certain embodiments, thevirtual skeleton model captures the topology and major geometry of thewhole blood vessel network including any aneurysm present in the bloodvessel network. In certain embodiments, the virtual skeleton modelrepresents not only healthy vessels but also unhealthy vessels such asan aneurysm to create a complete virtual picture of the blood vesselnetwork. In certain embodiments, the virtual skeleton model represents aplurality of skeleton points. Each skeleton point is defined by aplurality of blood vessel surface points. The plurality of blood vesselsurface points corresponds to points on the images of the blood vesselnetwork.

In certain embodiments, the skeletonization module 102 employs analgorithm to create the virtual skeleton model. In certain embodiments,the skeletonization module 102 employs a mean curvature skeleton methodfor creating the virtual skeleton model. In certain embodiments, themean curvature skeleton method comprises a single method or acombination of more than one method. In embodiments comprising more thanone method, the methods may be employed in series or in parallel. Forexample, the first method and the second method may be employedsimultaneously to create the virtual skeleton model.

In certain embodiments, the virtual skeleton model is created by themean curvature skeleton method grasping the topology of the blood vesselnetwork as well as essential geometric features of the blood vesselnetwork such as big bulges, but ignores small geometric features of theblood vessel network including “noisy” artifacts. The resulting virtualskeleton model is a skeleton of the blood vessel network and is in aform of a tree graph structure. In certain embodiments, tree graph edgesthat connect nodes represent center lines of the blood vessel surface.In certain embodiments, points that belong to a single edge representterminal points. In certain embodiments, points that belong to more thantwo edges are represented as a bifurcation. In certain embodiments, thetree graph is broken into edges based on the bifurcations.

As will be explained in more detail with respect to FIGS. 5A and 5B, theinlet/outlet module 104 is configured to identify inlets and outlets ofthe vasculature within the virtual skeleton model created by theskeletonization module 102. The inlets and outlets denote blood flowdirection into and out of the virtual skeleton model of the vasculature.In certain embodiments, the identification of the inlets and outlets isperformed automatically. In certain embodiments, the identification ofthe inlets and outlets is performed semi-automatically. In certainembodiments, the identification of the inlet is performedsemi-automatically while the identification of the outlet is performedautomatically. In certain embodiments, the identification of the outletis performed semi-automatically while the identification of the inlet isperformed automatically.

In certain embodiments, the inlet/outlet module 104 identifies theinlets and outlets based on geometry of the virtual skeleton model. Forexample, in certain embodiments, the inlet/outlet module 104 identifiesinlets and outlets based at least in part on characteristics of theplurality of skeleton points determined by the skeletonization module102.

Exemplary methods for identifying an inlet include a plane method and adefault method. However, the method for identifying the inlet is notlimited to the recited methods and includes changes and modifications tothe disclosed methods that would be known by a person having ordinaryskill in the art. The method can include identifying a cutting planebased on specific geometry of the surface mesh. In certain embodiments,the identified specific geometry are large, flat regions on the surfacemesh. Once the cutting plane is identified, the inlets are identified asterminal points within a certain distance of the identified cuttingplane.

As will be explained in more detail with respect to FIGS. 6A and 6B, theconstruction module 106 is configured to fit one or more predefinedgeometric shapes to the virtual skeleton model created by theskeletonization module 102. In certain embodiments, the constructionmodule 106 is configured to fit the one or more predefined geometricshapes to each edge created by the skeletonization module 102. Incertain embodiments, at least one of the predefined geometric shapes isan ellipse. Adjacent fitted ellipses may form elliptically shapedtubules. Of course the method is not limited to constructing and fittingellipses and may include other shapes as well as combinations ofdifferent shapes.

In certain embodiments, the construction module 106 fits the ellipses ina plane that is orthogonal to a tangent vector of the edge. In certainembodiments, the tangent vector is smoothed before defining theorthogonal plane. In certain embodiments, the smoothing is a one-stepaveraging smoothing.

In certain embodiments, the construction model 106 determines statisticsrelated to the one or more predefined geometric shapes fitted to thevirtual skeleton model. In certain embodiments, the statistics are usedby, for example, the filtering module 108. These statistics can includemean, minimum, maximum, and standard deviations for the parameters ofeach of the one or more predefined geometric shape fitted along eachedge. Of course, these statistics are not limited to the listedstatistics and can include other statistics that would be known by aperson having ordinary skill in the art.

As will be explained in more detail with respect to FIGS. 7A and 7B, thefiltering module 108 is configured to identify potential aneurysmswithin the virtual skeleton model of the blood vessel network. Incertain embodiments, the filtering module 108 analyzes the one or morepredefined geometric shapes fitted by the construction module 106 to thevirtual skeleton model along with one or more of the statisticsgenerated for the parameters of the one or more predefined geometricshapes determined by the construction model 106.

In certain embodiments, the filtering module 108 perform one or morefiltering steps to the virtual skeleton model so as to identifypotential aneurysms within the virtual skeleton model. In certainembodiments, the one or more filtering steps are based on one or more ofthe statistics determined by the construction model 106. In certainembodiments, the filtering module 108 filters the virtual skeleton modelbased on one or more statistics for the parameters of each of the one ormore predefined geometric shapes fitted along each edge. In certainembodiments, the one or more statistics include the mean, minimum,maximum, and standard deviations of the parameters for each of the oneor more predefined geometric shapes. In certain embodiments, one or moreof the steps within the one or more filtering steps are repeated oriterated to further identify additional potential aneurysms in thevirtual skeleton model.

In certain embodiments, the one or more filtering steps include a stepbased on ellipse area statistics. For example, filtering based onellipse area statistics can include statistically filtering each edgebased on the ellipse area. In certain embodiments, the one or morefiltering steps include a step based on ellipse eccentricity statistics.The variation in the eccentricity may be indicative of a potentialaneurysm even though there is little deviation in the area acrossadjacent ellipses.

In certain embodiments, the filtering module 108 flags skeleton pointswithin the virtual skeleton model as potential aneurysm and then removesthe flagged skeleton points from the virtual module based on acomparison of the eccentricity calculated for the ellipse and aneccentricity threshold value.

In certain embodiments, the filtering module 108 determines one or morevolumes. The filtering module 108 determined one or more volumes relatedto the potential aneurysms. In certain embodiments, the filtering module108 compares the volume of a healthy vessel in the region of thepotential aneurysms with the volume of the potential aneurysms.

The comparison between the volumes indicates whether the potentialaneurysms is a true positive or is a false positive. In certainembodiments, if the volume of the potential aneurysm is not greater thansome tolerance plus the virtual volume of the healthy vessel in theregion of the potential aneurysms then the potential aneurysms is afalse positive.

Aneurysm Measurement

As will be further explained below, in certain embodiments after thedetection steps are performed, identified potential aneurysms are shownin the display device 112 for final selection by the healthcareprovider. The healthcare provider screens out false positives from thelist of potential aneurysms using the user input 114. In certainembodiments, there are no more than a few false positives per case andthose false positives are easily screened out by the healthcare providervia the display device 112. In certain embodiments, the user input 114allows the healthcare provider to adjust the type of aneurysm (fusiformor saccular) for the aneurysms selected.

Aneurysm Comparison

As will be further explained below, given multiple scans of the samepatient taken over a period of time, the healthcare provider can trackand monitor the aneurysm growth. Certain features of the skeleton graph(for example, bifurcations and graph structure) created by theskeletonization module 102 will remain relatively constant, even if thepositions, orientations, or extent of vasculature differ between thesegmentations. In certain embodiments, these features are used to orientthe two geometries with respect to one another. After the geometries areoriented, the identified aneurysms from each geometry are matched andmeasurements are compared.

Some embodiments of the system 100, which is used for analysis and/ordisplay of vascular information for a patient, include some or all ofthe components shown in FIG. 1. The image apparatus 110 is used todetermine, analyze, and/or display vascular information for a patient.In certain embodiments, the image apparatus 110 is a Computed Tomography(CT) or Magnetic Resonance (MR) apparatus. In certain embodiments, theimage apparatus 110 produces 3D image data or scans for the patient. Insome examples, this data is in the form of a series of cross-sectionaldata scans. This data is represented, for instance through a process ofresampling or other form of image processing. In certain embodiments,the image apparatus 110 provides the 3D image data or scans to theskeletonization module 102 for further processing.

In the system shown in FIG. 1, the display device 112 is used to producea presentation image for presentation to a healthcare provider. Forinstance, the presentation image shows the results of one or more of theskeletonization module 102, the inlet/outlet module 104, theconstruction module 106, and the filtering module 108. In certainembodiments, the presentation image is a view of a three-dimensionalimage. In certain embodiments, an originally acquired image is enhancedto indicate locations of potential aneurysms and/or regions of thevasculature that have a high degree of abnormality.

In the system shown in FIG. 1, the user input 114 allows the healthcareprovider to review, analyze, modify, and select at least a portion ofthe results from one or more of the skeletonization module 102, theinlet/outlet module 104, the construction module 106, and the filteringmodule 108. In certain embodiments, the healthcare providerconfirms/re-selects inlet ports identified by the inlet/outlet module104 and/or screens false positives identified by the filtering module108 via the user input 114.

FIG. 2 is an exemplary representation of any of the modules of thecomputer system 100 including the skeletonization module 102, theinlet/outlet module 104, the construction module 106, and the filteringmodule 108 from FIG. 1.

The skeletonization module 102, the inlet/outlet module 104, theconstruction module 106, and/or the filtering module 108 can eachcomprise, inter alia, a central processing unit (CPU) 202, a memory 204,and a display 208. A bus input/output (I/O) interface 210 couples theskeletonization module 102, the inlet/outlet module 104, theconstruction module 106, and/or the filtering module 108. In certainembodiments, each of the skeletonization module 102, the inlet/outletmodule 104, the construction module 106, and/or the filtering module 108are generally coupled to various input devices 206 such as a mouse andkeyboard through the bus I/O interface 210 to the display 208. Incertain embodiments, one or more of the skeletonization module 102, theinlet/outlet module 104, the construction module 106, and/or thefiltering module 108 share a common input device 206 and/or display 208.In certain embodiments, the input device 206 is the same as the userinput 114 described with respect to FIG. 1. In certain embodiments, thedisplay 208 is the same as the display device 112 described with respectto FIG. 1.

The bus I/O interface 210 can include circuits such as cache, powersupplies, clock circuits, and a communications bus. The memory 204 caninclude RAM, ROM, disk drive, tape drive, etc., or a combinationthereof. Exemplary disclosed embodiments may be implemented as asubroutine, routine, program, software, stored in the memory 204 (e.g.,a non-transitory computer-readable storage medium) and executed by theCPU 202 to process data. As such, any of the skeletonization module 102,the inlet/outlet module 104, the construction module 106, and/or thefiltering module 108 can be implemented as a general-purpose computersystem that becomes a specific purpose computer system when executingthe subroutine, routine, program, software, stored in the memory 204.

In certain embodiments, any of the skeletonization module 102, theinlet/outlet module 104, the construction module 106, and/or thefiltering module 108 can also include an operating system andmicro-instruction code. The various processes and functions describedherein may either be part of the micro-instruction code or part of theapplication program (or a combination thereof) which is executed via theoperating system. In addition, various other peripheral devices may beconnected to the computer system 100 such as an additional data storagedevice and a printing device.

FIG. 3 is a representation of an exemplary graph 300 of a blood vesselnetwork 302 showing certain objects of the blood vessel network 302. Theblood vessel network 302 in FIG. 3 is simplified in that the bloodvessel network 302 is represented by points and lines. A visualrepresentation of surfaces of the blood vessel network 302 is notprovided in FIG. 3. However, as will be explained with respect to FIG.4, the graph 300 is derived from geometric features such as, forexample, surfaces and shapes representing the blood vessel network 302of the patient. In certain embodiments, the graph 300 is a mathematicalstructure used to model pairwise relations between objects of the bloodvessel network 302. Exemplary objects include nodes or points 304 andvertices 314. Other objects are within the scope of this disclosure.

In certain embodiments, the node or point 304 is classified as abifurcation point 306 or a terminal point 308. The classification candepend on, for example, the node or point 304 connection(s) and positionwithin the blood vessel network 302. In certain embodiments, pairwiserelations between relations itself are called links, lines, or edges310. For ease of explanation, a node or point 304 classified as abifurcation point 306 in the context of the graph 300 is a node or point304 that is attached to more than two edges 310. For example, FIG. 3illustrates two bifurcation points 306.

In certain embodiments, the node or point 304 classified as a terminalpoint 308 in the context of the graph 300 is a node or point 304 that isattached to only one edge 310. For example, FIG. 3 illustrates fourterminal points 308.

In certain embodiments, a path is defined as at least one edge 310connecting two nodes or points 304. Thus, each edge 310 is a path. Ifthe path includes a plurality of edges 310 and a plurality of nodes orpoints 304 from which the path can connect a vertex 314 to itself, thepath is then classified as a cycle 312. An exemplary cycle 312 isillustrated in FIG. 3.

FIG. 4A illustrates a virtual skeleton model 400. The virtual skeletonmodel 400 is created by the skeletonization module 102 from an exemplaryblood vessel network 402 of a patient. In certain embodiments, theskeletonization module 102 is configured to create the virtual skeletonmodel 400 of the blood vessel network 402. The blood vessel network 402of the patient is shown for convenience to understand the processperformed by the skeletonization module 102 to create the virtualskeleton model 400 of the blood vessel network 402. Once created and incertain embodiments, the virtual skeleton module 400 represents some orall of the geometric features of the blood vessel network 402. Incertain embodiments, the virtual skeleton module 400 represents a subsetof the geometric features of the blood vessel network 402.

In certain embodiments, the virtual skeleton model 400 is represented bya graph 404. An exemplary graph 300 is illustrated in FIG. 3. In certainembodiments, the graph 404 is a mathematical structure used to modelpairwise relations between objects that represent geometric features ofthe blood vessel network 402. Objects can include, for example as isillustrated in FIG. 3, nodes/points 304 and vertices 314 within thevirtual skeleton model 400.

In certain embodiments, the nodes or points 304 are classified as abifurcation point 306 or a terminal point 308. In FIG. 4A, multiplenodes or points 304 and identified as terminal points 308. As explainedwith respect to FIG. 3, pairwise relations between relations such asnodes or points 304 are called edges 310. Edges 310 can include links orlines within the virtual skeleton model 400.

In certain embodiments, the virtual skeleton model 400 represents atleast a portion of the blood vessel network 402. In certain embodiments,the blood vessel network 402 is created in part based on images taken ofthe blood vessel network 402. In certain embodiments, the imageapparatus 110 from FIG. 1 provides the images used to create a model ofthe blood vessel network 402. In certain embodiments, the imageapparatus 110 provides images from 3D rotational-angiography (3DRA),computed tomography angiography (CTA), and/or magnetic resonanceangiography (MRA). In certain embodiments, images provided by the imageapparatus 110 provide different geometric points of view of the bloodvessel network 402.

From the model of the blood vessel network 402, the skeletonizationmodule 102 creates the virtual skeleton model 400. In certainembodiments, the virtual skeleton model 400 represents the entire bloodvessel network 402. For example, the virtual skeleton model 400 canrepresent the blood vessel network 402 of the entire body of thepatient. In certain embodiments, the virtual skeleton model 400 capturesthe topology and major geometry of the entire blood vessel network 402including any potential aneurysm present in the entire blood vesselnetwork 402. In certain embodiments, the virtual skeleton model 400represents a subset of the blood vessel network 402. In certainembodiments, the virtual skeleton model 400 represents the blood vesselnetwork 402 in a targeted region of the body. For example in certainembodiments, the virtual skeleton model 400 represents the blood vesselnetwork 402 in the brain.

In certain embodiments, the virtual skeleton model 400 represents notonly healthy blood vessels but also unhealthy blood vessels. In thisway, the virtual skeleton model 400 includes unhealthy blood vessels,such as a potential aneurysm, to create a complete virtual skeletonmodel 400 of the blood vessel network 402.

In certain embodiments, the virtual skeleton model 400 is represented bythe graph 404. In certain embodiments, the graph 404 comprises one ormore edges 310 as described with respect to FIG. 3. Each edge 310 of thegraph 404 can comprise or be associated with one or more skeleton points406. In this way, a series of adjacent skeleton points 406 defines atleast a portion of the edge 310.

In certain embodiments, adjacent skeleton points 406 are separated by adistance. In certain embodiments, the distance between a first skeletonpoint 406 and an adjacent skeleton point 406 is predetermined. Incertain embodiments, the distance between the first skeleton point 406and the adjacent skeleton point 406 is determined during theskeletonization process. In certain embodiments, the distance betweenadjacent skeleton points 406 varies along the edge 310 comprising theadjacent skeleton points 406.

In certain embodiments, one or more intermediate skeleton points 406between the first skeleton point 406 and the second skeleton point 406is determined. For example in certain embodiments, the one or moreintermediate points 406 are determined based on interpolation. Incertain embodiments, the interpolation is based on one or more skeletonpoints 406 of the edge 310.

FIG. 4B is a view of a portion of the blood vessel network 402 takenalong lines 4B-4B of FIG. 4A and illustrates one or more surface points408 of the blood vessel network 402 and a single skeleton point 406associated with the one or more surface points 408. As disclosed above,the edge 310 of the graph 404 comprises one or more skeleton points 406.In certain embodiments, each of the skeleton points 406 is associatedwith or represents one or more surface points 408. In this way, theskeletonization module 102 processes the images provided by the imageapparatus 110 to create surfaces of the blood vessel network 402.

The surface of the blood vessel network 402 comprises or is representedby the one or more surface points 408. The one or more surface points408 correspond to points on one or more images of the blood vesselnetwork 402. Based at least in part on the one or more surface points408, the skeletonization module 102 determines the one or more skeletonpoints 406. In this way, a portion of the surface of the entire bloodvessel network 402 is efficiently represented by a single skeleton point406.

The number of surface points 408 represented by a single skeleton point406 can vary. In certain embodiments, a single skeleton point 406represents 100 surface points 408. In certain embodiments, a singleskeleton point 406 represents 1,000 surface points 408. In certainembodiments, a single skeleton point 406 represents 10,000 surfacepoints 408.

In certain embodiments, each skeleton point 406 represents surfacepoints 408 in proximity to the skeleton point 406. For example, theskeleton point 406 can represent a plurality of surface points 408 inthe region of or in close proximity to the skeleton point 406. In FIG.4B, the skeleton point 406 represents a plurality of surface points 408defining a circumference of the blood vessel network 402 proximal to theskeleton point 406. Of course, the disclosure is not so limited. Incertain embodiments, the plurality of surface points 408 and spaced fromtheir associated skeleton point 406.

In certain embodiments, not all the surface points 408 within aplurality of surface points 408 associated with a single skeleton point406 need have the same properties or geometric features. In certainembodiments, the surface points 408 associated with the same skeletonpoint 406 have different properties or geometric features. In certainembodiments, the different properties or geometric features for thesurface points 408 associated with the same skeleton point 406 are atleast a factor employed by the skeletonization module 102 whendetermining or defining the skeleton point 406 which will represent thesurface points 408.

In certain embodiments, a first weight is associated with a certainproperty or geometric feature of a first surface point 408 and a secondweight is associated with a certain property or geometric feature of asecond surface point 408. In certain embodiments, the weights are afactor used by the skeletonization module 102 when determining theskeleton point 406 to associate with the first and second surface points408.

In certain embodiments, the skeletonization module 102 employs analgorithm to create the virtual skeleton model 400. In certainembodiments, the skeletonization module 102 employs a mean curvaturemethod 410 for creating the virtual skeleton model 400.

In certain embodiments, the mean curvature method 410 comprises a singlemethod or algorithm or a combination of more than one method oralgorithm. In embodiments comprising more than one method, the methodsmay be employed in series in certain embodiments, in parallel in certainembodiments, concurrently or overlapping in duration in certainembodiments, and simultaneously in certain embodiments. For example, thefirst method and the second method may be employed simultaneously tocreate the virtual skeleton model 400.

In certain embodiments, the mean curvature method 410 comprises a firstmethod and a second method. In certain embodiments, the first method isa medial axis method. In certain embodiments, the medial axis method isbased on a medial axis transform (“MAT”). In certain embodiments, thevirtual skeleton model 400 created by the medial axis method provides avirtual skeleton model 400 that includes points at centers of maximallyinscribed spheres. In certain embodiments, to create the virtualskeleton model 400, the medial axis method grasps all of the geometry ofthe surface shape. The grasp shape is a true dual shape representation.By grasping all of the geometry, the virtual skeleton model 400 willinclude any “noisy” artifacts of the surface shape of the blood vesselnetwork 402 in the virtual skeleton model 400.

In certain embodiments where the mean curvature method 410 comprises afirst method and a second method, the second method can be a meancurvature flow method. In certain embodiments, the mean curvature flowmethod grasps selected portions and types of topology of the surfaceshape of the blood vessel network 402. In certain embodiments, theselected portions can include or exclude geometric features of thesurface shape. In certain embodiments, the selected portions can includeor exclude “noisy” artifacts of the surface shape. In certainembodiments, the selected portions can include or exclude the entiretopology of the surface shape. In certain embodiments, the second methodgrasps the topology of the surface shape of the blood vessel network 402and ignores all or almost all “noisy” artifacts. In certain embodiments,the second method grasps the topology of the surface shape of the bloodvessel network 402 and ignores all or almost all geometric features ofthe surface shape.

In certain embodiments, the virtual skeleton model 400 is created by themean curvature method 410 grasping the topology of the blood vesselnetwork 402 as well as essential geometric features of the blood vesselnetwork 402. In certain embodiments, exemplary essential geometricfeatures include big bulges, but ignores small geometric features of theblood vessel network including “noisy” artifacts. The resulting virtualskeleton model 400 is a skeleton of the blood vessel network 402 and isin a form of the graph 404.

In certain embodiments, the edges 310 that connect terminal points 308represent center lines of surfaces of the blood vessel network 402. Incertain embodiments, nodes or points 304 that belong to a single edge310 represent terminal points 308. In certain embodiments, nodes orpoints 304 that belong to more than two edges 310 are represented as abifurcation point 306. In certain embodiments, the graph 404 is brokeninto edge 310 based on the bifurcation points 306 within the graph 404.

FIG. 5A illustrates a virtual inlet/outlet model 500 of the blood vesselnetwork 402 illustrated in FIG. 4A. The inlet/outlet module 104illustrated in FIG. 1 is configured to identify inlet points 502 andoutlet points 504 of the blood vessel network 402 within the virtualskeleton module 400 created by the skeletonization module 102. Theinlets 502 and outlets 504 denote blood flow direction into and out ofthe virtual skeleton module 400 of the blood vessel network 402.

In certain embodiments, the identification of the inlets 502 and outlets504 is performed automatically. In certain embodiments, theidentification of the inlets 502 and outlets 504 is performedsemi-automatically. In certain embodiments, the identification of theinlet 502 is performed semi-automatically while the identification ofthe outlet 504 is performed automatically. In certain embodiments, theidentification of the outlet 504 is performed semi-automatically whilethe identification of the inlet 502 is performed automatically.

In certain embodiments, the inlet/outlet module 104 identifies theinlets 502 and outlets 504 based on geometry of the virtual skeletonmodel 400. For example, in certain embodiments, the inlet/outlet module104 identifies inlets 502 and outlets 504 based at least in part oncharacteristics of the plurality of skeleton points 406 determined bythe skeletonization module 102.

Exemplary methods for identifying an inlet 502 include a plane methodand a default method. However, the method for identifying the inlet 502is not limited to the recited methods and includes changes andmodifications to the disclosed methods that would be known by a personhaving ordinary skill in the art.

The plane method can include identifying a cutting plane based onspecific geometry of the surface mesh of the blood vessel network 402.In certain embodiments, the identified specific geometry are large, flatregions on the surface mesh of the blood vessel network 402. Once thecutting plane is identified, the inlets 502 are identified as terminalpoints 308 within a certain distance of the identified cutting plane andwith r_avg>than a certain value. In certain embodiments, the distance isa range between any of 2 mm and 10 mm, 3 mm and 9 mm, 4 mm and 8 mm, 5mm and 7 mm, and 6 mm. In certain embodiments, the distance is a value.The value can be 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8 mm, 9 mm, or 10mm. In certain embodiments, the certain value that r_avg is greater thanany of 0.5 mm, 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8 mm, 9 mm, and10 mm. In certain embodiments, inlets 502 are identified as terminalpoints 308 that are within 5 mm of the cutting plane and with r_avg>1mm.

The default method is employed when the plane method is unable toidentify the inlets 502. For example, in cases where the terminal points308 that are candidates for being an inlet 502 are outside the certaindistance specified by the plane method, the default method ignores thecertain distance requirement and identifies the terminal points 308 thatare candidates for being an outlet point 504 as an inlet point 502. Forexample, if the plane method fails, any identified outlet 504 withr_avg>1 mm is considered an inlet 502. A window is presented on thedisplay device 112 for the healthcare provider to provide user input114. In this way, the healthcare provider accepts/makes changes to theinlets 502 before proceeding.

Exemplary methods for identifying the outlets 504 include identifyingterminal points 308 on edges 310 that are identified by theskeletonization module 102 as having small skeleton aspect ratioaccording to the formula:

${{AR_{s}} = \frac{r_{avg}}{L}},$

where r_(avg) is the average distance to the blood vessel surface forthe edge 310 and L is the length of the edge 310. However, the methodfor identifying the outlet 504 is not limited to the recited method andincludes changes and modifications to the disclosed method that would beknown by a person having ordinary skill in the art.

In certain embodiments, each skeleton point 406 of the plurality ofskeleton points 406 is classified based on whether the skeleton point406 is associated with one edge 310 within the virtual skeleton model400 or with more than one edge 310 within the virtual skeleton model400. In certain embodiments, skeleton points 406 associated with asingle edge 310 are identified as terminal points 308 by theinlet/outlet module 104. The inlet/outlet module 104 classifies theterminal points 308 as an inlet 502 or an outlet 504 into the bloodvessel network 402.

The edges 310 are generated through the virtual skeleton module 400 fromthe inlets 502 to the outlets 504. In certain embodiments, segmentationissues can arise in which loops or cycles 312 are incorrectly createdwithin the virtual skeleton module 400. These incorrect loops or cycles312 can occur due to a false connection between edges 310.

In certain embodiments as is illustrated in FIG. 5B, the blood vesselnetwork 400 is initially masked by the inlet/outlet module 104 as bloodvessel 506, cycle 312, or potential aneurysm 508.

As is illustrated in FIG. 6A, the construction module 106 fits one ormore predefined geometric shapes to the virtual skeleton model 400created by the skeletonization module 102. In the embodiment illustratedin FIG. 6A, the at least one predefined geometric shape is an ellipse602 and forms an ellipse model 600. In certain embodiments, for eachskeleton point 406 along the edge 310 an appropriately sized oreffective ellipse 602 is fitted. In certain embodiments, for eachskeleton point 406 along the edge 310, the appropriately sized effectiveellipse 602 is fitted through the surface points 408 corresponding tothe skeleton point 406. Of course, the method is not limited toconstructing and fitting ellipses 602 and may include other shapes aswell as combinations of different shapes.

A plurality of ellipses 602 fitted along a plurality of skeleton points406 of the edge 310 together form an elliptically shaped tube 606. InFIG. 6A, the fitted ellipses 602 form the elliptically shaped tubules606. Thus, the elliptically shaped tubules 606 are represented as a setof ellipses 602 along the edge 310. Of course, the method is not limitedto forming elliptically shaped tubules 606 from a plurality of adjacentellipses 602 and may include other shapes as well as combinations ofdifferent shapes.

In certain embodiments, the construction module 106 fits the ellipses602 in a plane that is orthogonal to a tangent vector of the edge 310.In certain embodiments, the tangent vector is smoothed before definingthe orthogonal plane. In certain embodiments, the smoothing is aone-step averaging smoothing.

As is illustrated in FIG. 6B, the construction module 106 fits anellipse 602 to one of the skeleton points 406 of the edge 310. Theskeleton point 406 represents one or more of the surface points 408. Incertain embodiments, the surface points 408 with high surface curvaturevalues 604 are excluded by the construction module 106 when theconstruction module 106 fits the ellipse 602. The surface points 408with high curvature values 604 may represent “noise” artifacts and notactual surface points 408 of the blood vessel network 402.

In certain embodiments, the construction model 106 determines statisticsrelated to the one or more predefined geometric shapes fitted to thevirtual skeleton model 400. In certain embodiments, the statistics areused by, for example, the filtering module 108. These statistics caninclude mean, minimum, maximum, and standard deviations for each of theone or more predefined geometric shapes fitted along each edge 310. Ofcourse, these statistics are not limited to the listed statistics andcan include other statistics that would be known by a person havingordinary skill in the art.

FIG. 7A illustrates the output of the filtering module 108 representedby a filter model 700. The filter model 700 in FIG. 7A is created byfiltering the ellipse model 600 from FIG. 6A. In certain embodiments,the filtering module 108 is configured to further identify potentialaneurysms 704 from the potential aneurysms 508 within the virtualskeleton model 400 of the blood vessel network 402. In certainembodiments, the filtering module 108 analyzes the one or morepredefined geometric shapes, such as ellipses 602, fitted by theconstruction module 106 to the virtual skeleton model 400 along with oneor more of the statistics generated for the one or more predefinedgeometric shapes, such as ellipses 602, determined by the constructionmodel 106.

In certain embodiments, the one or more filtering steps are based on oneor more of the statistics determined by the construction model 106. Incertain embodiments, the filtering module 108 filters the virtualskeleton model 400 based on one or more statistics for each of the oneor more predefined geometric shapes, such as ellipses 602, fitted ateach skeleton point 406 along each edge 310. In certain embodiments, theone or more statistics include one or more of the mean, minimum,maximum, and standard deviations for each of the one or more predefinedgeometric shapes, such as ellipses 602. In certain embodiments, one ormore of the steps within the one or more filtering steps are repeated oriterated to further identify one or more of the ellipses 602 as either avessel ellipse 702 or a potential aneurysm 704 in the virtual skeletonmodel 400.

In certain embodiments, the one or more filtering steps include a stepbased on ellipse area statistics. FIG. 7B illustrates ellipse areasassociated with skeleton points 406 along the edge 310. A first line inFIG. 7B is identified as “before filtering” and a second line isidentified as “after area filtering”. Notably, the after area filteringline does not include removed skeleton points 406 flagged as potentialaneurysms 704. The after area filtering line includes only skeletonpoints 406 associated with healthy blood vessel network 402 or vesselellipses 702.

In certain embodiments, for example, filtering based on an area ofellipse 602 statistics can include statistically filtering each skeletonpoint 406 along the edge 310 based on the areas of the ellipses 602associated with the skeleton points 406 of the edge 310.

In certain embodiments, a skeleton point 406 within the virtual skeletonmodel 400 is flagged as a potential aneurysm 704 based on the area ofthe ellipse 602. For example, in certain embodiments, ifA _(i) >A _(mean)+2*A _(sd)

where A_(mean) is the mean area for the edge 310 and A_(sd) is thestandard deviation then the skeleton point 406 is flagged as a potentialaneurysm 704. Once the skeleton point 406 and the surface points 408 andellipse 602 associated with the skeleton point 406 are flagged as apotential aneurysm 704 and not as a vessel ellipse 702, the skeletonpoint 406 is removed from the virtual skeleton model 400. In certainembodiments, for sufficiently long sections (>5 mm) of the edge 310,A_(mean) is set using a linear regression, allowing it to reduce overthe length of the section.

In certain embodiments, for skeleton points 406 and the surface points408 and ellipses 602 associated with the skeleton points 406 that arenot identified as potential aneurysms 704, the ellipses 602 associatedwith the skeleton points 406 are identified as vessel ellipses 702.

In certain embodiments, one or more constraints are placed on A_(mean)to ensure that the areas of the ellipses 602 behave realistically overthe entire path of the edge 310. The one or more constraints can relateto whether the skeleton point 406 is located at a junction within thevirtual skeleton model 400. In certain embodiments, the junction is at abifurcation point 306 or at a confluence within the virtual skeletonmodel 400.

In certain embodiments for bifurcation, the additional constraint is:A _(mean)=mean(A _(mean),1.2*A _(healthy)),

where A_(healthy) is the previous healthy section mean. This ensures theoverall trend is for the area to decrease.

In certain embodiments for confluences, the additional constraint is:

A_(mean) is set based on the maximum A_(healthy) of all the inlets 502.This prevents confluences from being flagged as false positives.

In certain embodiments, multiple passes of area filtering are performedon the skeleton points 406 and associated ellipses 602 to ensure thatthe final statistics represent the healthy blood vessel network 402through that region.

In certain embodiments, edges 312 with A_(max)<π mm² are statisticallyunlikely to contain aneurysms and are ignored.

FIG. 8A illustrates the output of the filtering module 108 after areafiltering of the ellipse 602. In FIG. 8A, the illustrated ellipses 602have been flagged as vessel ellipses 702, not potential aneurysms 704.

FIG. 8B illustrates the output of the filtering module 108 after furtherfiltering the output in FIG. 8A for variations in eccentricity. Incertain embodiments, the additional pass of eccentricity filteringcaptures regions of high eccentricity that were not flagged during areafiltering. As is illustrated in FIG. 8B, some of the ellipses 702 whichwere not flagged as potential aneurysms 704 in FIG. 8A based onvariations in adjacent areas of ellipses 702, were flagged as havinghigh variations in eccentricity between adjacent ellipses 702.

Thus, FIG. 8B illustrates in certain embodiments that the one or morefiltering steps includes a step based on ellipse eccentricitystatistics. For example, filtering based on eccentricity statistics ofthe ellipse 602 can include statistically filtering each edge 310 basedon the ellipse eccentricity. Filtering based on ellipse eccentricity mayidentify a region of the virtual skeleton model 400 as a potentialaneurysm 704. For example, even though the area of the adjacent ellipses602 in FIG. 8A along the edge 310 do not vary enough for the filteringmodel 108 to flag the area as a potential aneurysm 704 based on ellipsearea statistics, the filtering module 108 may flag the area based onvariations in the eccentricity of the adjacent ellipses 602. Thevariation in the eccentricity may be indicative of a potential aneurysm704 even though there is little deviation in the area across adjacentellipses 602. In certain embodiments, the filtering module 108determines the eccentricity of the ellipse 602 by the equation:

${E = \frac{a}{b}},$where a and b are the major and minor radii of the ellipse 602.

In certain embodiments, a skeleton point 406 within the virtual skeletonmodel 400 is flagged as a potential aneurysm 704 based on theeccentricity of the ellipse 602. In certain embodiments, the filteringmodule 108 removes the flagged skeleton points 406 from the virtualskeleton model 400 based on a comparison of the eccentricity calculatedfor the ellipse 602 and an eccentricity threshold value. In certainembodiments, the eccentricity threshold value is predetermined. Incertain embodiments, the eccentricity threshold value is initiallypredetermined but then is updated based on measured data related to theblood vessel network 402.

In certain embodiments, the threshold eccentricity threshold value isany one of 1.1, 1.2, 1.3, 1.4, and 1.5. In certain embodiments, thefiltering module 108 flags skeleton points 406 associated with anellipse 602 with eccentricity that exceeds the eccentricity thresholdvalue. For example, if the eccentricity of the ellipse 602 associatedwith a skeleton point 406 exceeds E_(i)>1.3, the skeleton point 406 isremoved from the virtual skeleton model 400 and identified as apotential aneurysm 704. In certain embodiments, the filtering pass bythe filtering module 108 may also pick up very ovalized portions of theblood vessel network 402.

In certain embodiments, if the filtering module 108 determines that theellipses 602 associated with skeleton points 406 along an edge 310 haveeccentricity values that do not exceed a max threshold value, that edge310 likely does not contain an aneurysm 704 and may be ignored. Incertain embodiments, the max threshold value can be any one of 1.1, 1.2,1.3, 1.4, 1.5, and 1.6. In certain embodiments, the max threshold valuefor the entire edge 310 is:E _(max)<1.4.

FIG. 9A illustrates the output of the filtering module 108 after volumefiltering of a first potential aneurysm 704. Volumetric filteringdemonstrates the first potential aneurysm 704 is a true positiveaneurysm 900. FIG. 9B illustrates the output of the filtering module 108after volume filtering of a second potential aneurysm 704. Volumetricfiltering demonstrates the second potential aneurysm 704 is a falsepositive aneurysm 902.

In certain embodiments, the one or more filtering steps includes a stepfor filtering the skeleton points 406 that have been identified aspotential aneurysms 704 and removed from the virtual skeleton model 400by the filtering module 108. In certain embodiments, the potentialaneurysms 704 were identified by the filtering module 108 during thefiltering step based on ellipse area statistics (FIGS. 7A and 7B) and/orduring the filtering step based on ellipse eccentricity statistics(FIGS. 8A and 8B). In this way, the filtering module 108 filters outfalse positive identified by the filtering module 108 during thefiltering step based on ellipse area statistics and/or during thefiltering step based on ellipse eccentricity statistics.

In certain embodiments, the filtering module 108 determines one or morevolumes. The filtering module 108 determines one or more volumes relatedto the potential aneurysms 704. In certain embodiments, the filteringmodule 108 compares the volume of a healthy vessel in the region of thepotential aneurysms 704 with the volume of the potential aneurysms 704.

In certain embodiments, the filtering module 108 computes a cylindricalvolume using the ellipse areas 704 in the region of the potentialaneurysm 704 to represent the volume of the potential aneurysm 704. Incertain embodiments, the filtering module 108 computes a cylindricalvolume in the region of the potential aneurysm 704 by interpolating theareas of the ellipses 702 on the sides of the potential aneurysm 704into the region of the potential aneurysm 704. In this way, theinterpolated volume represents the virtual volume of a healthy vessel inthe area of the potential aneurysm 704.

In certain embodiments, the comparison between the cylindrical volumesindicates whether the potential aneurysm 704 is a true positive 900 oris a false positive 902. In certain embodiments, if the volume of thepotential aneurysm 704 is not more than 15% greater than the virtualvolume of the healthy vessel in the region of the potential aneurysm 704then the potential aneurysm 704 is a false positive 902. In certainembodiments, if the volume of the potential aneurysm 704 is not morethan 10% greater than the virtual volume of the healthy vessel in theregion of the potential aneurysm 704 then the potential aneurysm 704 isa false positive 902. In certain embodiments, if the volume of thepotential aneurysm 704 is not more than 5% greater than the virtualvolume of the healthy vessel in the region of the potential aneurysm 704then the potential aneurysm 704 is a false positive 902. In certainembodiments, the filtering module 108 applies the equation:

V_(e)<1.1*V_(h), where V_(e) is potential aneurysm volume, V_(h) ishealthy virtual volume.

The filtering module 108 compares the volumes and identifies thepotential aneurysm 704 as a false positive 902 if the volumes satisfythe equation.

In certain embodiments, if the calculated volume of the potentialaneurysm 704 is less than the equation:

V_(e)<π mm³, the potential aneurysm 704 is considered too small torepresent a true positive 900 and is identified as a false positive 902.

Aneurysm Measurements

FIG. 10A illustrates idealized saccular measurements 1000 of a potentialaneurysm 508. FIG. 10B illustrates idealized fusiform measurements 1010of a potential aneurysm 508. In certain embodiments after the detectionsteps are performed, identified potential aneurysms 508 are shown in thedisplay device 112 for final selection by the healthcare provider. Thehealthcare provider screens out false positives 902 from the list ofpotential aneurysms 508 using the user input 114. In certainembodiments, there are no more than a few false positives 902 per caseand those false positives 902 are easily screened out by the healthcareprovider via the display device 112. In certain embodiments, the userinput 114 allows the healthcare provider to adjust the type of aneurysm(fusiform or saccular) for the aneurysms selected.

In certain embodiments as is illustrated in FIG. 10A, saccular formmeasurements 1000 include automatic neck-plane identification 1006, neckwidth 1008, dome height 1004, and dome width 1002.

In certain embodiments as is illustrated in FIG. 10B, fusiform formmeasurements 1010 include automatic distal 1016 and proximal plane 1018identification, fusiform length 1014, and fusiform width 1012.

Aneurysm Comparison

FIGS. 11A and 11B illustrate changes in dimensions of a saccularaneurysm over time. The scan illustrated in FIG. 11B is of the sameidentified aneurysm 508(a), 508(b) illustrated in FIG. 11A but after aperiod of time. FIG. 11C illustrates the same identified aneurysm508(a), 508(b) from scans taken at different times overlapped to showgrowth of the identified aneurysm 508(a), 508(b). In certain embodimentsas is illustrated in FIG. 11A, saccular form measurements 1100 includeautomatic neck-plane identification 1006(a), neck width 1008(a), domeheight 1004(a), and dome width 1002(a). In certain embodiments as isillustrated in FIG. 11B, saccular form measurements 1102 includeautomatic neck-plane identification 1006(b), neck width 1008(b), domeheight 1004(b), and dome width 1002(b).

A comparison between FIGS. 11A and 11B of the two scans of the sameidentified aneurysm 508(a), 508(b) shows growth in the identifiedaneurysm 508(a), 508(b) occurring over time. Given multiple scans of thesame identified aneurysm 508(a), 508(b) taken over a period of time, thehealthcare provider can track and monitor growth of the aneurysm 508(a),508(b).

In certain embodiments, some features of the virtual skeleton model 400created by the skeletonization module 102 will remain relativelyconstant, even if the positions, orientations, or extent of vasculaturediffer between the segmentations. In certain embodiments, these constantfeatures include bifurcation points 306 and graph structure. Of course,features of the virtual skeleton model 400 that are relatively constant,such as terminal points 308, edges 310, cycles 312, and vertices 314 canalso be used to orient the identified aneurysm 508(a), 508(b) fallwithin the scope of this disclosure. In certain embodiments, thesefeatures are used to orient the two geometries of the two scans of thesame identified aneurysm 508(a), 508(b) with respect to one another.After the geometries are oriented, the identified aneurysms 508(a),508(b) from each geometry are matched and measurements are compared.

FIGS. 12A and 12B illustrate exemplary measurements and dimensions of asaccular aneurysm. The neck plane 1006 is defined as a best fit planethat encompasses the boundary between the aneurysmal sac and the parentvessel. As is illustrated in FIG. 12B, in certain embodiments, the neckplane 1006 is fitted on a set of “neck” points 1202 on a surface of thesaccular aneurysm. Neck points 1202 with negative Gaussian curvature aregiven additional weight proportional to the magnitude of the Gaussiancurvature to define the location of the neck plane 1006. The degree ofcurvature of each neck point is reflected by a color of the neck point1202 in FIG. 12B.

The dome height 1004 is defined as a maximum distance between a centerof the neck plane 1006 and a surface point on the aneurysmal sac of theaneurysm 508. The dome width 1002 is defined as a maximum width of theidentified aneurysm 508 measured perpendicular to the line defining thedome height 1004.

FIG. 13 illustrates exemplary measurements and dimensions of a fusiformaneurysm. A distal plane 1016 is defined as a plane positioned along theedge 310 or centerline that defines the fusiform aneurysm. The locationof the distal plane 1016 is fitted in a similar way as the neck plane1006 is fitted for the saccular aneurysm illustrated in FIG. 12B. Aproximal plane 1018 is defined as a plane positioned along the edge 310or centerline that defines the fusiform aneurysm. The location of theproximal plane 1018 is fitted in a similar way as the neck plane 1006 isfitted for the saccular aneurysm illustrated in FIG. 12B.

A length 1014 of the fusiform aneurysm is measured along the edge 310 ofthe virtual skeleton model 400 between the distal plane 1016 and theproximal plane 1018. In certain embodiments, the path points undergoseveral iterations of moving average smoothing to produce a more directpath than the virtual skeleton model 400.

A width 1012 of the fusiform aneurysm is measured perpendicular to thesmoothed path between the distal plane 1016 and the proximal plane 1018.

FIG. 14 illustrates a method for cerebral aneurysm detection performedby the computer system 100 illustrated in FIG. 1. The process begins atstep 1402 where the image apparatus 110 performs image segmentation andoutputs blood vessel surfaces of the blood vessel network 402. The imageapparatus 110 determined, analyzed, and/or displays vascular informationfor a patient. In certain embodiments, the image apparatus 110 is aComputed Tomography (CT) or Magnetic Resonance (MR) apparatus. Incertain embodiments, the image apparatus 110 produces 3D image data orscans for the patient. In some examples, this data is in the form of aseries of cross-sectional data scans. This data is represented, forinstance through a process of resampling or other form of imageprocessing. In certain embodiments, the image apparatus 110 from FIG. 1provides the images used to create the model of the blood vessel network402. In certain embodiments, images provided by the image apparatus 110provide different geometric points of view of the blood vessel network402

The method continues to step 1404 where the skeletonization module 102skeletonizes the blood vessel surfaces of the blood vessel network 402.In certain embodiments, the computer system 110 provides the 3D imagedata or scans to the skeletonization module 102 for further processing.The skeletonization module 102 creates the virtual skeleton model 400from the blood vessel network 402 of the patient. Once created and incertain embodiments, the virtual skeleton module 400 represents some orall of the geometric features of the blood vessel network 402. Incertain embodiments, the virtual skeleton module 400 represents a subsetof the geometric features of the blood vessel network 402.

In certain embodiments, the virtual skeleton model 400 is represented bya graph 404. In certain embodiments, the graph 404 is a mathematicalstructure used to model pairwise relations between objects thatrepresent geometric features of the blood vessel network 402. In certainembodiments, the virtual skeleton model 400 represents at least aportion of the blood vessel network 402.

From the model of the blood vessel network 402, the skeletonizationmodule 102 creates the virtual skeleton model 400. In certainembodiments, the virtual skeleton model 400 represents the entire bloodvessel network 402. For example, the virtual skeleton model 400 canrepresent the blood vessel network 402 of the entire body of thepatient. In certain embodiments, the virtual skeleton model 400 capturesthe topology and major geometry of the entire blood vessel network 402including any potential aneurysm present in the entire blood vesselnetwork 402. In certain embodiments, the virtual skeleton model 400represents a subset of the blood vessel network 402. In certainembodiments, the virtual skeleton model 400 represents the blood vesselnetwork 402 in a targeted region of the body. For example in certainembodiments, the virtual skeleton model 400 represents the blood vesselnetwork 402 in the brain.

In certain embodiments, the virtual skeleton model 400 represents notonly healthy blood vessels but also unhealthy blood vessels. In thisway, the virtual skeleton model 400 includes unhealthy blood vessels,such as a potential aneurysm, to create a complete virtual skeletonmodel 400 of the blood vessel network 402.

The method continues to step 1406 where the inlet/outlet module 104identifies inlets 502 and outlets 504 of the vasculature within thevirtual skeleton model 400 created by the skeletonization module 102.The inlets 502 and outlets 504 denote blood flow direction into and outof the virtual skeleton model 400 of the vasculature. In certainembodiments, the identification of the inlets 502 and outlets 504 isperformed automatically. In certain embodiments, the identification ofthe inlets 502 and outlets 504 is performed semi-automatically.

In certain embodiments, the inlet/outlet module 104 identifies theinlets 502 and outlets 504 based on geometry of the virtual skeletonmodel 400. For example, in certain embodiments, the inlet/outlet module104 identifies inlets 502 and outlets 504 based at least in part oncharacteristics of the plurality of skeleton points 406 determined bythe skeletonization module 102.

The method continues to step 1408 where a user may interact with thecomputer system 100 via the display device 112. Further, the user mayrepetitively interact with the display device 112 regarding the proposedselections of the inlets 502 and outlets 504 by the computer system 100.In this way, the healthcare provider confirms or re-selects the inlets502 and/or outlets 504.

The method continues to step 1410 where the construction module 106 fitsone or more predefined geometric shapes to the virtual skeleton modelcreated by the skeletonization module 102. In certain embodiments, theconstruction module 106 is configured to fit the one or more predefinedgeometric shapes to each edge 310 created by the skeletonization module102. In certain embodiments, at least one of the predefined geometricshapes is an ellipse 602. Of course the method is not limited toconstructing and fitting ellipses 602 and may include other shapes aswell as combinations of different shapes.

In certain embodiments, the construction module 106 fits the ellipses602 in a plane that is orthogonal to a tangent vector of the edge 310.In certain embodiments, the tangent vector is smoothed before definingthe orthogonal plane. In certain embodiments, the smoothing is aone-step averaging smoothing.

The method continues to step 1412. At step 1412, the filtering module108 identifies potential aneurysms 508 within the virtual skeleton model400 of the blood vessel network 402. In certain embodiments, thefiltering module 108 analyzes the one or more predefined geometricshapes fitted by the construction module 106 to the virtual skeletonmodel 400 along with one or more of the statistics generated for the oneor more predefined geometric shapes determined by the construction model106.

In certain embodiments, the filtering module 108 performs one or morefiltering steps to the virtual skeleton model 400 so as to identifypotential aneurysms 508 within the virtual skeleton model 400. Incertain embodiments, the one or more filtering steps are based on one ormore of the statistics determined by the construction model 106. Incertain embodiments, the filtering module 108 filters the virtualskeleton model 400 based on one or more statistics for each of the oneor more predefined geometric shapes fitted along each edge 310. Incertain embodiments, the one or more statistics include the mean,minimum, maximum, and standard deviations for each of the one or morepredefined geometric shapes. In certain embodiments, one or more of thesteps 1412 within the one or more filtering steps are repeated oriterated to further identify additional potential aneurysms 508 in thevirtual skeleton model 400.

In certain embodiments, the one or more filtering steps 1412 include astep based on ellipse area statistics. In certain embodiments, the oneor more filtering steps 1412 include a step based on ellipseeccentricity statistics. Filtering based on ellipse eccentricity mayidentify a region of the virtual skeleton model 400 as a potentialaneurysm 508.

In certain embodiments, the filtering module 108 flags skeleton points406 within the virtual skeleton model 400 as potential aneurysm 508 andthen removes the flagged skeleton points 406 from the virtual skeletonmodule 400 based on a comparison of the eccentricity calculated for theellipse 602 and an eccentricity threshold value.

The method continues to step 1414 where the filtering module 108determines one or more volumes related to the potential aneurysm 508. Incertain embodiments, the filtering module 108 compares the volume of ahealthy vessel in the region of the potential aneurysms 508 with thevolume of the potential aneurysms 508.

In certain embodiments, the filtering module 108 computes a cylindricalvolume using the ellipse areas in the region of the potential aneurysm508 to represent the volume 704 of the potential aneurysm 508. Incertain embodiments, the filtering module 108 computes a cylindricalvolume in the region of the potential aneurysm 508 by interpolating theareas of the ellipses on the sides of the potential aneurysm 508 intothe region of the potential aneurysm 508. In this way, the interpolatedvolume 702 represents the virtual volume of a healthy vessel in the areaof the potential aneurysm 508.

The comparison between the volumes 702, 704 indicates whether thepotential aneurysm 508 is a true positive 900 or is a false positive902. In certain embodiments, if the volume of the potential aneurysm 508is less than 1.1*the virtual volume of the healthy vessel 702 in theregion of the potential aneurysm 508 then the potential aneurysm 508 isa false positive 902.

The method continues to step 1416 where the identified potentialaneurysms 508, 704 are shown in the display device 112 for finalselection by the healthcare provider. At step 1418, the healthcareprovider screens out false positives 902 from the list of potentialaneurysms 508, 704 using the user input 114. In certain embodiments,there are no more than a few false positives 902 per case and thosefalse positives 902 are easily screened out by the healthcare providervia the display device 112. In certain embodiments, the user input 114allows the healthcare provider to adjust the type of aneurysm (fusiform1010 or saccular 1000) for the aneurysms selected.

FIG. 15 illustrates a method performed by the skeletonization model 102for forming a skeletonized graph or virtual skeleton model 400. Incertain embodiments, the skeletonization module 102 employs a meancurvature method 410 for creating the virtual skeleton model 400. Incertain embodiments, the mean curvature method 410 comprises a singlemethod or algorithm or a combination of more than one method oralgorithm. In embodiments comprising more than one method, the methodsmay be employed in series in certain embodiments, in parallel in certainembodiments, concurrently or overlapping in duration in certainembodiments, and simultaneously in certain embodiments. For example, thefirst method and the second method may be employed simultaneously tocreate the virtual skeleton model 400.

In certain embodiments, the mean curvature method 410 comprises a firstmethod and a second method. In certain embodiments, the first method isa medial axis method and the second method is a mean curvature flowmethod.

The method begins at step 1502 where the skeletonization module 102determines a plurality of blood vessel surface points 408 from the imagesegmentation of the surface of the blood vessel network 402. The methodcontinues to steps 1504 and 1506 where the skeletonization module 102determines a medial axial transform of the image segmentation and a meancurvature flow of the image segmentation, respectively.

In certain embodiments, steps 1504 and 1506 are employed in series, inparallel, concurrently or overlapping in duration, or simultaneously.For example, steps 1504 and 1506 may be employed simultaneously.

The method continues to step 1508 where the skeletonization module 102determines a plurality of skeleton points 406 from the plurality ofblood vessel surface points 408. The method continues to step 1510 wherethe skeletonization module 102 associates each skeleton point 406 of theplurality of skeleton points 406 with a subset of the plurality of bloodvessel surface points 408. The method continues to step 1512 where theskeletonization module 102 determines a plurality of edge-lines 310 fromthe plurality of skeleton points 406. The method continues to step 1514where the skeletonization module 102 forms the skeletonized graph orvirtual skeleton model 400.

FIG. 16 illustrates a method performed by the inlet/outlet module 104 toidentify inlets points 502 and outlets points 504 of the skeletonizedgraph or virtual skeleton model 400. The method begins at step 1602where the skeletonized graph or virtual skeleton model 400 is providedto the inlet/outlet module 104. In certain embodiments, the virtualskeleton model 400 comprises a plurality of edge 310. Each edge 310 hasa plurality of skeleton points 406. Each skeleton point 406 isassociated with a subset of a plurality of blood vessel surface points408. The method continues to step 1604 where the inlet/outlet module 104determines a distance from each inlet 502 to a flat region formed by oneor more of the plurality of blood vessel surface points 408. The methodcontinues to step 1606 where the inlet/outlet module 104 determines anaverage distance from the edge 310 to the subset of the plurality ofblood vessel surface points 408 associated with the edge 310. The methodcontinues to step 1608 where the inlet/outlet module 104 determines askeleton aspect ratio for each edge 310. The method continues to step1610 where the inlet/outlet module 104 identifies inlets 502 and outlets504 of the skeletonized graph or virtual skeleton model 400.

FIG. 17 illustrates a method performed by the construction module 106 toidentify potential aneurysm 508. The method begins at step 1702 wherethe construction module 106 is provided with a skeletonized graph orvirtual skeleton model 400. In certain embodiments, the virtual skeletonmodel 400 comprises a plurality of edge 310. Each edge 310 has aplurality of skeleton points 406. Each skeleton point 406 is associatedwith a subset of a plurality of blood vessel surface points 408. Themethod continues to step 1704 where the construction module 106virtually fits ellipses 602 to form elliptically shaped tubules 606 foreach edge 310 of the skeletonized graph or virtual skeleton model 400through corresponding blood vessel surface points 408. The methodcontinues to step 1706 where the construction module 106 determinesmean, minimum, maximum, and standard deviations for the geometricparameters for each edge 310. The method continuous to step 1708 wherethe construction module 106 identifies a plurality of potentialaneurysms 508 based on the determined statistics.

FIG. 18 illustrates a method performed by the filtering module 108 tofilter potential aneurysm 508 identified by the construction module 106.The method begins at step 1802 where the filtering module 108 identifiesa plurality of potential aneurysms 508 and determined statistics of theplurality of potential aneurysms 508. The method continues to step 1804where the filtering module 108 determines deviations of areas betweenadjacent ellipses 602 of the elliptically shaped tubules 606. The methodcontinues to step 1806 where the filtering module 108 determineseccentricity statistics for each ellipse 602 along the edge 310. Themethod continues to step 1808 where the filtering module 108 filters theplurality of potential aneurysms 508 based on the determined deviationsof area and the determined eccentricities. The method continues to step1810 where the filtering module 108 determines volume statistics foreach ellipse 602 along the edge 310. The method continues to step 1812where the filtering module 108 filters the plurality of potentialaneurysms 508 to identify false positives (FP) aneurysm cases based onthe determined volume statistics.

FIG. 19 illustrates a method of identifying a cerebral aneurysm from animage segmentation. In certain embodiments, the method is performed bythe computer system 100 illustrated in FIG. 1. The process begins atstep 1902 with providing an image segmentation of a surface of a bloodvessel network 402. The image segmentation comprises a plurality ofblood vessel surface points 408. The method continuous to step 1904 withforming a skeletonized graph or virtual skeleton model 400 from theimage segmentation. The skeletonized graph or virtual skeleton model 400comprises a plurality of edge 310. The method continues to step 1906with identifying inlets 502 and outlets 504 of the skeletonized graph orvirtual skeleton model 400. The method continues to step 1908 withvirtually fitting elliptically shaped tubules 606 comprising ellipses602 for each edge 310 of the skeletonized graph or virtual skeletonmodel 400. The method continues to step 1910 with determining statisticsof the fitted elliptically shaped tubules for each edge 310. The methodcontinues to step 1912 with identifying a plurality of potentialaneurysms 508 based on the determined statistics. The method continuesto step 1914 with filtering the plurality of potential aneurysms 508based on the determined statistics. The method continues to step 1916with identifying the cerebral aneurysms based at least in part on thefiltering.

FIG. 20 illustrates a method of measuring a cerebral aneurysm dependingon whether the aneurysm is a saccular or fusiform aneurysm. The methodbegins at step 2002 where the filtering module 108 identifies apotential aneurysm 508. The method continues to step 2004 where thefiltering module 108 determines whether the potential aneurysm 508 is asaccular aneurysm type or a fusiform aneurism type. The method continuesto step 2006 where the filtering module 108 determines a neck plane1006, a neck width 1008 of the neck plane 1006, a dome height 1004, anddome width 1002 of the potential aneurysm 508 if it is a saccularaneurysm. The method continues to step 2008 where the filtering module108 determines a distal plane 1016, a proximal plane 1018, a fusiformlength 1014, and a fusiform width 1012 of the potential aneurysm 508 ifit is a fusiform aneurysm.

FIG. 21 illustrates a method for comparing geometry of a cerebralaneurysm over a period of time. The method begins at step 2102 withproviding a first image segmentation of a surface of a blood vesselnetwork 402. The method continues to step 2104 with forming a firstskeletonized graph from the first image segmentation. The firstskeletonized graph comprises a plurality of edges 310. The methodcontinues to step 2106 with identifying one or more first nodes withinthe first skeletonized graph or virtual skeleton model 400. Each of theone or more first nodes intersecting more than two edges 310 of theplurality of edge 310. The method continues to step 2108 withidentifying one or more first terminal points within the firstskeletonized graph. Each of the one or more first terminal pointsintersecting only one edge 310 of the plurality of edges 310. The methodcontinues to step 2110 with providing a second image segmentation of asurface of the blood vessel network 402. The method continues to step2112 with forming a second skeletonized graph from the second imagesegmentation. The second skeletonized graph comprises a plurality ofedges 310. The method continues to step 2114 with identifying one ormore second nodes within the second skeletonized graph or virtualskeleton model 400. Each of the one or more second nodes intersectingmore than two edges 310 of the plurality of edge 310. The methodcontinues to step 2116 with identifying one or more second terminalpoints within the second skeletonized graph. Each of the one or moresecond terminal points intersecting only one edge 310 of the pluralityof edges 310. The method continues to step 2118 with overlapping thefirst skeletonized graph and the second skeletonized graph by orientingthe one or more first nodes with the one or more second nodes andorienting the one or more first terminal points with the one or moresecond terminal points. The method continues to step 2120 with comparingthe geometry of the cerebral aneurysm when the first skeletonized graphis overlapped with the second skeletonized graph.

Advantages of certain embodiments disclosed herein include automaticallyidentify the aneurysm; using statistics to pare down to the healthyanatomy and so automatically define thresholds which could begeneralized for other regions of the anatomy; and using statisticalfiltering to identify abnormalities in the vasculature.

In certain embodiments, the disclosed invention is related to CAD(Computer Aided Detection) method for cerebral aneurysmdetection/analysis and measurement. Major features, that each in itselfand all together, comprise the novelty of the method compared to priorart include (1) skeletonization based on the “Mean Curvature Skeleton”for the entire blood vessel network from the segmented image. Althoughprevious arts in the field used center line computation, with theemployment of the MAT only (medial axis transform and its derivative),usually the purpose of center lines was to represent “healthy” vesselswhere big aneurysms were pre-screened out already. In the presenteddisclosure a combination of MAT and Curvature Flow “Mean CurvatureSkeleton” is employed to capture the topology and major geometry of thewhole vessel network, excluding noises, in the segmented images; (2)identifying inlets/outlets of the vasculature by using skeletoninformation obtained in step 1, all possible inlets/outlets areidentified automatically and presented for final approval/adjustment bythe user; (3) construct elliptically shaped tubules for each edge; (4)compute statistics of fitted ellipse parameters for each edge-line; (5)perform iterative filtering of aneurysm based on ellipse area statisticsfor each edge-line; (6) perform filtering of aneurysm based on ellipseeccentricity statistics for each edge-line; and (7) filter out falsepositive (FP) aneurysm cases by volumetric ellipse tubules filtering.Steps (3)-(7) are based on using fitted ellipses as the “crosssectional” shape of the blood vessel. Prior art has not fitted ellipsesin a consistent and systematic way to detect the aneurysm itself. Alsothe “cross-section” points in certain embodiments are at each centerlinepoint, not the geometric cross section, but the set of points thatcollapsed to that skeleton point.

In certain embodiments of the aneurysm measurement, the automaticidentification of the separation plane (for saccular form aneurysm),utilizing points weighted by negative surface Gaussian curvature values(“neck points”) is advantageous. In certain embodiments for aneurysmcomparison, the use of the skeleton features to orient multiplegeometries with respect to each other is advantageous. This allows forautomatic tracking and comparison of aneurysms over time.

The terms “processor”, as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refer without limitation to a computer system, statemachine, processor, or the like designed to perform arithmetic or logicoperations using logic circuitry that responds to and processes thebasic instructions that drive a computer. In some embodiments, the termscan include ROM and/or RAM associated therewith.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical steps, blocks, modules and circuitsdescribed in connection with the present disclosure (such as the stepsof FIGS. 1 and 9) may be implemented or performed with a general purposeprocessor, GPU computational units (using CUDA or OpenCL), or part ofthe computation may be performed on a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array signal (FPGA) or other programmable logic device (PLD),discrete gate or transistor logic, discrete hardware components or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects computer readable medium may comprisenon-transitory computer readable medium (e.g., tangible media). Inaddition, in some aspects computer readable medium may comprisetransitory computer readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by an electronic communicationdevice as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that anelectronic communication device can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

The foregoing illustrated embodiments have been provided solely forillustrating the functional principles of the present invention and arenot intended to be limiting. For example, the present invention may bepracticed using different overall structural configuration andmaterials. Persons skilled in the art will appreciate that modificationsand alterations of the embodiments described herein can be made withoutdeparting from the spirit, principles, or scope of the presentinvention. The present invention is intended to encompass allmodifications, substitutions, alterations, and equivalents within thespirit and scope of the following appended claims.

What is claimed is:
 1. A method for identifying growth of a cerebralaneurysm, the method comprising: providing a first image segmentation ofa surface of a blood vessel network, the first image segmentationcomprising a first plurality of blood vessel surface points; forming afirst virtual skeleton model from the first image segmentation, thefirst virtual skeleton model comprising a plurality of edges, each edgeof the plurality of edges having a plurality of skeleton points, eachskeleton point of the plurality of skeleton points being associated witha subset of the plurality of blood vessel surface points; identifyingone or more first nodes within the first virtual skeleton model, each ofthe one or more first nodes intersecting more than two edges of theplurality of edges; providing a second image segmentation of a surfaceof the blood vessel network, the second image segmentation comprising asecond plurality of blood vessel surface points; forming a secondvirtual skeleton model from the second image segmentation, the secondvirtual skeleton model comprising a plurality of edges, each edge of theplurality of edges having a plurality of skeleton points, each skeletonpoint of the plurality of skeleton points being associated with a subsetof the plurality of blood vessel surface points; identifying one or moresecond nodes within the second virtual skeleton model, each of the oneor more second nodes intersecting more than two edges of the pluralityof edges; and overlapping the first virtual skeleton model and thesecond virtual skeleton model by orienting the one or more first nodeswith the one or more second nodes.
 2. The method of claim 1, furthercomprising comparing geometry of the cerebral aneurysm when the firstvirtual skeleton model is overlapped with the second virtual skeletonmodel.
 3. The method of claim 1, further comprising: identifying one ormore first terminal points within the first virtual skeleton model, eachof the one or more first terminal points intersecting only one edge ofthe plurality of edges; identifying one or more second terminal pointswithin the second virtual skeleton model, each of the one or more secondterminal points intersecting only one edge of the plurality of edges;and overlapping the first virtual skeleton model and the second virtualskeleton model by orienting the one or more first terminal points withthe one or more second terminal points.
 4. The method of claim 1,wherein the one or more first nodes is a bifurcation point.
 5. Themethod of claim 1, wherein the one or more first nodes is part of acycle.
 6. A system for identifying growth of a cerebral aneurysm, thesystem comprising: One or more processors configured to: provide a firstimage segmentation of a surface of a blood vessel network, the firstimage segmentation comprising a first plurality of blood vessel surfacepoints; form a first virtual skeleton model from the first imagesegmentation, the first virtual skeleton model comprising a plurality ofedges, each edge of the plurality of edges having a plurality ofskeleton points, each skeleton point of the plurality of skeleton pointsbeing associated with a subset of the plurality of blood vessel surfacepoints; identify one or more first nodes within the first virtualskeleton model, each of the one or more first nodes intersecting morethan two edges of the plurality of edges; provide a second imagesegmentation of a surface of the blood vessel network, the second imagesegmentation comprising a second plurality of blood vessel surfacepoints; form a second virtual skeleton model from the second imagesegmentation, the second virtual skeleton model comprising a pluralityof edges, each edge of the plurality of edges having a plurality ofskeleton points, each skeleton point of the plurality of skeleton pointsbeing associated with a subset of the plurality of blood vessel surfacepoints; identify one or more second nodes within the second virtualskeleton model, each of the one or more second nodes intersecting morethan two edges of the plurality of edges; and overlap the first virtualskeleton model and the second virtual skeleton model by orienting theone or more first nodes with the one or more second nodes.
 7. The systemof claim 6, wherein the one or more processors are further configured tocompare geometry of the cerebral aneurysm when the first virtualskeleton model is overlapped with the second virtual skeleton model. 8.The system of claim 6, wherein the one or more processors are furtherconfigured to: identify one or more first terminal points within thefirst virtual skeleton model, each of the one or more first terminalpoints intersecting only one edge of the plurality of edges; identifyone or more second terminal points within the second virtual skeletonmodel, each of the one or more second terminal points intersecting onlyone edge of the plurality of edges; and overlap the first virtualskeleton model and the second virtual skeleton model by orienting theone or more first terminal points with the one or more second terminalpoints.
 9. The system of claim 6, wherein the one or more first nodes isa bifurcation point.
 10. The system of claim 6, wherein the one or morefirst nodes is part of a cycle.
 11. The system of claim 6, wherein theone or more second nodes is a bifurcation point.
 12. The system of claim6, wherein the one or more second nodes is part of a cycle.
 13. A methodfor identifying growth of a cerebral aneurysm, the method comprising:providing a first image segmentation of a surface of a blood vesselnetwork, the first image segmentation comprising a first plurality ofblood vessel surface points; forming a first virtual skeleton model fromthe first image segmentation, the first virtual skeleton modelcomprising a plurality of edges, each edge of the plurality of edgeshaving a plurality of skeleton points, each skeleton point of theplurality of skeleton points being associated with a subset of theplurality of blood vessel surface points; identifying one or more firstterminal points within the first virtual skeleton model, each of the oneor more first terminal points intersecting only one edge of theplurality of edges; providing a second image segmentation of a surfaceof the blood vessel network, the second image segmentation comprising asecond plurality of blood vessel surface points; forming a secondvirtual skeleton model from the second image segmentation, the secondvirtual skeleton model comprising a plurality of edges, each edge of theplurality of edges having a plurality of skeleton points, each skeletonpoint of the plurality of skeleton points being associated with a subsetof the plurality of blood vessel surface points; identifying one or moresecond terminal points within the second virtual skeleton model, each ofthe one or more second terminal points intersecting only one edge of theplurality of edges; and overlapping the first virtual skeleton model andthe second virtual skeleton model by orienting the one or more firstterminal points with the one or more second terminal points.
 14. Themethod of claim 13, further comprising comparing geometry of thecerebral aneurysm when the first virtual skeleton model is overlappedwith the second virtual skeleton model.
 15. The method of claim 13,further comprising: identifying one or more first nodes within the firstvirtual skeleton model, each of the one or more first nodes intersectingmore than one edge of the plurality of edges; identifying one or moresecond nodes within the second virtual skeleton model, each of the oneor more second nodes intersecting more than one edge of the plurality ofedges; and overlapping the first virtual skeleton model and the secondvirtual skeleton model by orienting the one or more first nodes with theone or more second nodes.
 16. The method of claim 15, wherein the one ormore first nodes is a bifurcation point.
 17. The method of claim 15,wherein the one or more first nodes is part of a cycle.
 18. The methodof claim 15, wherein the one or more second nodes is a bifurcationpoint.
 19. The method of claim 15, wherein the one or more second nodesis part of a cycle.
 20. The method of claim 19, wherein the one or morefirst nodes is a vertex.